Alternatives Pharma is all too aware of this, which is why it uses qualitative data from web forums, social media and blogs in its efforts to help pharmaceutical marketing teams connect with both patients and doctors.
However, sourcing, collating and analyzing such data on a suitably large scale is impossible without the help of technology.
Rather than letting this data sit untapped, VOZIQ made use of it.
It integrated AI to analyze post-call comments, categorizing them by topic and flagging sentiment scores that indicate customer dissatisfaction and the likelihood of churn.
Building a business case for AI isn’t so different from building one for any other business problem.
First, identify a need and a desired outcome (automation and efficiency are common drivers of successful AI projects). You’ll need to determine whether you have enough data to work with and whether it’s the kind of data that lends itself to pattern identification and subsequent decision making.
Using a platform called Metis, Dorchester Collection parses, summarizes and contextualizes reviews in order to gain insights, plan next steps and maintain a competitive advantage.
The Problem: Creating Messaging That Resonates With Users What patients say in a clinical setting is different from what they say behind closed doors -- or in the anonymity of the internet.
To get the insights and in-depth analysis needed to improve pharmaceutical messaging and communications, Alternatives Pharma turned to AI.
This has allowed the company to analyze, categorize, and “theme” patients’ online discussions around particular diseases and pharmaceutical products.